As road traffic has continued to increase at rates greater than increases in road capacity, the effects of increasing traffic congestion have had growing deleterious effects on business and government operations and on personal well-being. Accordingly, efforts have been made to combat the increasing traffic congestion in various ways, such as by obtaining and providing information about current traffic conditions to individuals and organizations. One source for obtaining information about current traffic conditions in some larger metropolitan areas is networks of traffic sensors capable of measuring traffic flow for various roads in the area (e.g., via sensors embedded in the road pavement), and such current traffic condition information may be provided to interested parties in various ways (e.g., via frequent radio broadcasts, an Internet Web site that displays a map of a geographical area with color-coded information about current traffic congestion on some major roads in the geographical area, information sent to cellular telephones and other portable consumer devices, etc.). However, while such current traffic information provides some benefits in particular situations, the lack of accurate information about future traffic conditions creates a number of problems.
Accordingly, limited attempts have been made to generate and provide information about possible future traffic conditions, but such attempts have typically suffered from inaccuracies in the generated information, as well as various other problems. For example, some efforts to provide information about possible future traffic conditions have merely calculated and provided historical averages of accumulated data. While such historical averages may occasionally produce information for a particular place at a particular day and time that is temporarily similar to actual conditions, such historical averages cannot adapt to reflect specific current conditions that can greatly affect traffic (e.g., weather problems, traffic accidents, current road work, non-periodic events with large attendance, etc.), nor can they typically accommodate general changes over time in the amount of traffic, and thus the generated information is typically inaccurate and of little practical use for planning purposes.
Other prior efforts to provide information about possible future traffic conditions have utilized statistical methods to incorporate some current traffic and other condition information with historical traffic flow data to make a static projection related to a single possible future change in traffic flow. For example, for a particular road interval, such prior efforts may make an attempt to generate a single static projection of how long it will be until the amount of traffic flow on the road interval might change (e.g., to change from flowing poorly to flowing freely). However, even if such efforts to project an amount of time until traffic flow changes were able to provide accurate projections, such limited future information does not typically produce sufficient information in sufficient detail to allow detailed planning. For example, such systems cannot project future traffic conditions over each of multiple future time intervals for multiple roads in a road network, such as to allow an optimal path to accurately be identified through various roads in the network based on the projected information.
Thus, it would be beneficial to provide improved techniques for predicting future traffic conditions for multiple road segments at each of multiple future time intervals, such as based on past, current, and expected future traffic and other conditions, as well as to provide additional related capabilities.